MATHEMATICAL MODELING OF THE COVID-19 EPIDEMICS
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Abstract
Since the COVID-19 outbreak in Wuhan City in December 2019, numerous model predictions on the COVID-19 epidemics in Wuhan have been reported. These model predictions have shown a wide range of variations. In our first study, we demonstrate that nonidentifiability in model calibrations using the confirmed case data is the main reason for such wide variations. Our modeling study indicates that more independent datasets, better inference methods, and fitting algorithms can significantly reduce the nonidentifiable impact. Further study is carried out for modeling the first COVID-19 wave in Alberta. Confirmed case data and testing data, based on the official reports from Alberta Health, are fitted to a mathematical model estimating the total number of the COVID-19 infections. A sensitivity analysis using PRCC is conducted to show that decreasing the initial transmission rate and increasing the probability of health-seeking for an individual are the most effective ways to help control the epidemic.